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National science Review 2024年第8期11卷 7-8页

作者： Zongben Xu Zhi-Hua Zhou Wenwu Zhu School of Mathematics and Statistics Xi'an Jiaotong University National Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Tsinghua University

With the exponential growth of big data and advancements in large-scale foundation model techniques, the field of machine learning has embarked on an unprecedented golden era. This period is characterized by significant innovations across various aspects of machine learning, including data exploitation, network architecture development, loss function settings and algorithmic innovation.

关键词： automation learning machine

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Impact of transfer learning compared to convolutional neural networks on fruit detection

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 7791-7803页

作者： Salem, Dina Ahmed Hassan, Nesma Abdelaziz Hamdy, Razan Mohamed Computer and Software Engineering Department Misr University for Science and Technology Giza Egypt

Smart farming, also known as precision agriculture or digital farming, is an innovative approach to agriculture that utilizes advanced technologies and data-driven techniques to optimize various aspects of farming operations. One smart farming activity, fruit classification, has broad applications and impacts across agriculture, food production, health, research, and environmental conservation. Accurate and reliable fruit classification benefits various stakeholders, from farmers and food producers to consumers and conservationists. In this study, we conduct a comprehensive comparative analysis to assess the performance of a Convolutional Neural Network (CNN) model in conjunction with four transfer learning models: VGG16, ResNet50, MobileNet-V2, and EfficientNet-B0. Models are trained once on a benchmark dataset called Fruits360 and another time on a reduced version of it to study the effect of data size and image processing on fruit classification performance. The original dataset reported accuracy scores of 95%, 93%, 99.8%, 65%, and 92.6% for these models, respectively. While accuracy increased when trained on the reduced dataset for three of the employed models. This study provides valuable insights into the performance of various deep learning models and dataset versions, offering guidance on model selection and data preprocessing strategies for image classification tasks. © 2024-IOS Press. All rights reserved.

关键词： Fruits

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Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

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computers, Materials & Continua 2024年第10期81卷 707-748页

作者： Noor Ullah Bacha Songfeng Lu Attiq Ur Rehman Muhammad Idrees Yazeed Yasin Ghadi Tahani Jaser Alahmadi School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430073China Department of Computer Science and Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain12555Abu Dhabi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

关键词： Cross-site scripting machine learning XSS detection stacking ensemble learning hybrid learning

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Deep-BERT:Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

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computer Systems science & engineering 2023年第2期44卷 1775-1791页

作者： Md.Anwar Hussen Wadud M.F.Mridha Jungpil Shin Kamruddin Nur Aloke Kumar Saha Department of Computer Science and Engineering Bangladesh University of Business and TechnologyDhakaBangladesh School of Computer Science and Engineering University of AizuAizuwakamatsuJapan Department of Computer Science American International University-BangladeshDhakaBangladesh Department of Computer Science and Engineering University of Asia PacificDhakaBangladesh

Offensive messages on social media,have recently been frequently used to harass and criticize *** recent studies,many promising algorithms have been developed to identify offensive *** algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in *** addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive *** this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass *** paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based ***,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efﬁcacy outperformed all existing offensive text classiﬁcation algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.

关键词： Offensive text classiﬁcation deep convolutional neural network(DCNN) bidirectional encoder representations from transformers(BERT) natural language processing(NLP)

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Fake News Detection on Social Media Using Ensemble Methods

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computers, Materials & Continua 2024年第12期81卷 4525-4549页

作者： Muhammad Ali Ilyas Abdul Rehman Assad Abbas Dongsun Kim Muhammad Tahir Naseem Nasro Min Allah Department of Computer Science COMSATS UniversityIslamabad45550Pakistan School of Computer Science and Engineering Kyungpook National UniversityDaegu41566Republic of Korea Department of Computer Science and Engineering Korea UniversitySeoul02841Republic of Korea Department of Electronic Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam34223Saudi Arabia

In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive *** news,a prevalent issue,particularly on social media,complicates the assessment of news *** pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing *** the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful *** numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or *** study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news *** employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as Information Gain,Chi-Square,Principal Component Analysis(PCA),and Document *** comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news *** findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and ***,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world *** introduce a pioneering ensemble

关键词： Fake news detection Machine Learning(ML) Deep Learning(DL) Chi-Square ensembling

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A survey on rendering homogeneous participating media

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Computational Visual Media 2022年第2期8卷 177-198页

作者： Wenshi Wu Beibei Wang Ling-Qi Yan School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina University of California Santa BarbaraUSA

Participating media are frequent in real-world scenes,whether they contain milk,fruit juice,oil,or muddy water in a river or the *** light interacts with these participating media in complex ways:refraction at boundaries and scattering and absorption inside *** radiative transfer equation is the key to solving this *** are several categories of rendering methods which are all based on this equation,but using different *** this paper,we introduce these groups,which include volume density estimation based approaches,virtual point/ray/beam lights,point based approaches,Monte Carlo based approaches,acceleration techniques,accurate single scattering methods,neural network based methods,and spatially-correlated participating media related *** well as discussing these methods,we consider the challenges and open problems in this research area.

关键词： participating media Monte Carlo methods rendering volume density estimation

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Advancing skin cancer detection integrating a novel unsupervised classification and enhanced imaging techniques

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CAAI Transactions on Intelligence technology 2025年第2期10卷 474-493页

作者： MdAbdur Rahman Nur Mohammad Fahad Mohaimenul Azam Khan Raiaan Mirjam Jonkman Friso De Boer Sami Azam Department of Computer Science and Engineering United International UniversityDhakaBangladesh Faculty of Science and Technology Charles Darwin UniversityCasuarinaNorthern TerritoryAustralia

Skin cancer,a severe health threat,can spread rapidly if ***,early detection can lead to an advanced and efficient diagnosis,thus reducing *** classification techniques analyse extensive skin image datasets,identifying patterns and anomalies without prior labelling,facilitating early detection and effective diagnosis and potentially saving *** this study,the authors aim to explore the potential of unsupervised learning methods in classifying different types of skin lesions in dermatoscopic *** authors aim to bridge the gap in dermatological research by introducing innovative techniques that enhance image quality and improve feature *** achieve this,enhanced super-resolution generative adversarial networks(ESRGAN)was fine-tuned to strengthen the resolution of skin lesion images,making critical features more *** authors extracted histogram features to capture essential colour characteristics and used the Davies-Bouldin index and silhouette score to determine optimal ***-tuned k-means clustering with Euclidean distance in the histogram feature space achieved 87.77% and 90.5% test accuracies on the ISIC2019 and HAM10000 datasets,*** unsupervised approach effectively categorises skin lesions,indicating that unsupervised learning can significantly advance dermatology by enabling early detection and classification without extensive manual annotation.

关键词： histogram feature optimal cluster skin lesion unsupervised classification

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Rare earth lanthanum pinning effect for corrosion resistance ultraefficient microwave absorption FeCo@rGO composites

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Journal of Materials science & technology 2024年第10期177卷 181-190页

作者： Junru Yao Jintang Zhou Lu Lu Feng Yang Zhengjun Yao Bo Ouyang Erjun Kan Yuxin Zuo Renchao Che Fan Wu College of Materials Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211100China Ministry of Industry and Information Technology Key Laboratory of Material Preparation and Protection for Harsh Environment(Nanjing University of Aeronautics and Astronautics)Nanjing 211100China MIIT Key Laboratory of Semiconductor Microstructure and Quantum Sensing School of ScienceNanjing University of Science and TechnologyNanjing 210094China School of Chemistry and Chemical Engineering Anhui UniversityHefei 230601China Laboratory of Advanced Materials Shanghai Key Lab of Molecular Catalysis and Innovative MaterialsDepartment of Materials ScienceFudan UniversityShanghai 200438China School of Mechanical Engineering Nanjing University of Science and TechnologyNanjing 210094China

Magnetic metal absorbers exhibit remarkable microwave absorption ***,their practical application is severely limited due to their susceptibility to corrosion,particularly in marine *** address this challenge,we propose a novel approach involving the modification and control of FeCo/rGO microwave absorbers using rare earth lanthanum(La).This strategy aims to achieve both high-performance microwave absorption and enhanced resistance to marine *** this study,we employ a La_(2)O_(3) modifying control strategy to refine the FeCo magnetic particles and coat them with CoFe_(2)O_(4) on the surface,leveraging the pinning effect of in situ generated La_(2)O_(3) .This process enhances the interface polarization of the absorbers,thereby improving their electromagnetic performance and ma-rine corrosion ***,the La_(2)O_(3) modified FeCo@rGO composites exhibit broadband ab-sorption,covering a wide frequency range of 6.11 GHz at 1.55 ***,the electromagnetic proper-ties of the La_(2)O_(3) modified FeCo@rGO absorbers remain stable even after prolonged exposure to a 3.5 wt% NaCI solution,simulating marine conditions,for at least 15 ***,we perform first-principle calculations on FeCo and FeCo-O to validate the corrosion resistance of the La_(2)O_(3) modified FeCo@rGO composites at the atomic *** comprehensive investigation explores the control of rare earth lan-thanum modification on the size of magnetic metal particles,enabling efficient electromagnetic wave absorption and marine corrosion *** results of this study provide a novel and facile strategy for the control of microwave absorbers,offering promising prospects for future research and development in this field.

关键词： Rare earth lanthanum FeCo@rGO Microwave absorption Marine corrosion resistance

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Research on Low Earth Orbit Satellite Orbit Prediction Based on a Two-stage Integrated Model 24

Research on Low Earth Orbit Satellite Orbit Prediction Based...

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3rd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Yuan, Shuai Zhang, Kun School of Cyberspace Security Nanjing University of Science and Technology Jiangsu Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710834

With the number of low Earth orbit satellites and the expansion of application demands, satellite trajectory prediction is crucial for efficiently operating satellite systems. Traditional trajectory prediction methods rely on classical physical models such as Newtonian mechanics and celestial mechanics. Still, these methods have insufficient accuracy and real-time performance when facing low-orbit satellites’ complex in-orbit environments. Compared with traditional physical models, neural networks can better capture complex changes in trajectories and compensate for the shortcomings of traditional methods. This article analyzes the effects of atmospheric drag, solar radiation pressure, and other factors on the orbital dynamics of low Earth orbit satellites, proposes a trajectory prediction model based on deep neural networks, and uses Long Short-term Memory networks (LSTM) to improve prediction accuracy. To further improve prediction accuracy, this study adopted an ensemble learning strategy, integrating LSTM, GRU, and Transformer models to construct a two-stage ensemble model. The experimental results show that the fusion model significantly surpasses the single model in both short-term and long-term prediction tasks. In short-term forecasting, the combination of LSTM and GRU can efficiently capture rapid changes in trajectory data. In long-term forecasting, Transformer significantly reduces prediction errors through its powerful modeling capabilities. The two-stage integrated model performs excellently in satellite trajectory prediction, with RMSE and MAE reduced by 21.8% and 14.3%, respectively in short-term prediction compared to the LSTM model, and RMSE and MAE reduced by 4.5% and 10%, respectively in long-term prediction. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Enabling Secure Data Aggregation Scheme for Smart Grids based on Local Differential Privacy 24

Enabling Secure Data Aggregation Scheme for Smart Grids base...

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2nd International Conference on Electronics, computers and Communication technology, CECCT 2024

作者： He, Yixuan Mei, Lin Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology Jiangsu Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710193

Existing data aggregation schemes for privacy protection in smart grids face limitations in efficiency and application, often relying on trusted third parties or incurring significant overhead. To address these issues, this paper introduces a multidimensional and multisubset data aggregation scheme leveraging local differential privacy, aimed at enabling fine-grained data analysis while safeguarding privacy. The scheme effectively protects user privacy by locally perturbing data from randomly selected dimensions while utilizing the Chinese Remainder Theorem to compress multidimensional data, thereby optimizing the transmission. Theoretical and experimental results demonstrate that our scheme offers strong privacy, flexibility, and utility, achieving a relative error of aggregation results no more than 0.05 under optimal conditions. Additionally, it outperforms existing approaches in terms of both computational and communication overhead. © 2024 Copyright held by the owner/author(s).

关键词： Differential privacy

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